CN102843966B - For the treatment of the method and apparatus of cyclic physiological signal - Google Patents
For the treatment of the method and apparatus of cyclic physiological signal Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0255—Recording instruments specially adapted therefor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7221—Determining signal validity, reliability or quality
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
- A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The present invention relates to a kind of method and apparatus for the treatment of cyclic physiological signal (30,40,52,53,54).Said method comprising the steps of: containing described cyclic physiological signal (30,40,52,53,54) period (31,32 in two or more cycles, 33) repeatedly (2) described physiological signal (30,40,52 is collected in, 53,54), wherein next period (31,32,33) preceding epoch (31,32 is close to, 33) or overlapping with preceding epoch (31,32,33); Described physiological signal (30,40,52 within each period (31,32,33), 53,54) extract the value of (3,13) one group of predefine parameter in, the value of described parameter is characterized in described period (31,32,33) the described physiological signal (30,40,52,53,54) in; And to classify (4,14) described physiological signal (30,40,52,53,54) within each period (31,32,33) based on the value of this extracted group predefine parameter.This provide the efficient analysis of cyclic physiological signal, it is specially adapted to the continuous monitoring of the patient when the trend of reliable physiological signal is more important than the instant measurement of reliable physiological signal, such as, under the public ward environment and/or home environment of hospital.
Description
Technical field
The present invention relates to a kind of method and apparatus for the treatment of cyclic physiological signal.
Background technology
To vital sign patient, such as breathing rate and heart rate, the requirement that has under the Intensive Care Therapy environment of hospital of monitoring from different to the requirement of patient-monitoring in public ward scene or in home environment.The intensive care unit(ICU) of hospital requires instantaneity and the high reliability of institute's monitoring parameter, and more pays attention to the trend of institute's monitoring parameter under the public ward environment of hospital.
Such as, proved that breathing rate is the good instruction that status of patient worsens, and it serves extremely important effect in conjunction with in other vital signs in early days early warning hospital system.Thus, particularly in the Intensive care ward of hospital, think and need to carry out monitoring continuously and reliably to breath signal.In the public ward scene of hospital or in home health care application, such as, in tele-medicine and chronic disease management, also there are similar needs, it is to the reliability of institute's monitoring parameter and immediately present and have not too strict requirement.Although the continuous monitoring of breath signal to extracting breathing rate from it can be obtained from the bedside monitors for intensive care patient, also in the various portable sensor system of exploitation thus allow with minimum uncomfortable property to public ward in the breath signal of mobile patient carry out clog-free and long-term measurement and monitoring.
In patient-monitoring, motion artifacts is a well-known problem generally, and it refers to by the physiological signal pollution of patient body activity such as caused by postural change, motion and talk and the deterioration of measurement quality.Particularly for cyclic physiological signal, from contaminated cyclical signal, extract correct changeable frequency obtain very difficult.Motion artifacts problem under public ward scene than intensive care unit(ICU) scene under more obvious, because the patient under public ward scene has more mobile activity pattern usually, and in the monitored most of the time, do not have fixed nursing to monitor, thus lack about body movement existence and measure the information of background.Even become more serious in the patient-monitoring of this problem under home health care scene.
In order to allow the reliable use of this continuous monitor system, this motion artifacts problem must be solved.Various signal recuperation scheme is absorbed in the research that major part has been reported, and wherein normally application self-adapting noise eliminates the signal polluted by motion with cleaning.There is several very formidable intrinsic difficult point in these schemes.Such as, motion artifacts may be caused by multiple noise sources of very difficult identification and estimation.Another shortcoming is that these schemes need very large amount of calculation usually, and is thus that efficiency is not high for portable system.
US5546952 discloses a kind of method and apparatus for determining the effectiveness of respiratory waveform from the signal with non-respiratory artifact, comprises the waveform parameter of monitoring respiratory effort waveform, this parameter characterization non-respiratory artifact.By this parameter compared with predetermined boundary to determine whether effective respiratory waveform to be detected.This is useful in the treatment of obstructive sleep apnea, wherein, when effective respiratory effort waveform can be obtained, the electricity irritation of patient will be limited to the expiratory phase of breathing cycle validly, and when effective respiratory effort not detected, suppress electricity irritation.Selected monitoring waveform parameter can be such as Inspiratory rise time, inspiration peak time, air-breathing start the time, the time of air-breathing peak to peak, the time of expiration peak to peak that terminate to exhaling or breathe breathing time.The initialization of breath signal analytic process occurs when system is opened or reset, and wherein this system tracks several breathing cycle is to arrange amplifier gain and to set up the normal morphology parameter of waveform.Relative to last time air-breathing beginning and Time Created benchmark, thus can calculate the beginning of prediction of breathing next time, it stimulates and the breathing cycle for the synchronous electric when effective respiratory effort waveform.Although this method does not adopt adaptive noise cancel-ation, its object is still to provide the instant reliably breath signal based on peak one by one, and this is very important under Intensive Care Therapy scene, but it is not the prerequisite under public ward scene.
Summary of the invention
The object of the present invention is to provide a kind of efficient analysis of cyclic physiological signal, it is specially adapted to when the trend of reliable physiological signal is than the continuous monitoring of patient time more important based on the instant measurement of the reliable physiological signal at peak one by one, such as, under the general ward environment and/or home environment of hospital.
In a first aspect of the present invention, provide a kind of method for the treatment of cycle Sexual Physiology signal, described method comprises the steps:
-contain described cyclic physiological signal two or more cycles time interimly repeatedly collect described physiological signal, wherein next period contiguous preceding epoch or overlapping with preceding epoch;
-from the described physiological signal in each period, extract the value of one group of predefine parameter, described parameter characterizes the described physiological signal in described period according to time, frequency and locus; And
-described the physiological signal of classifying in each period based on the value of this extracted group predefine parameter.
Thus according to The inventive process provides a kind of different times to cyclic physiological signal or classification of frame based on one group of characterisitic parameter or feature, the value of this group characterisitic parameter or feature is wherein only extracted for that specific period of this physiological signal or section.Do not have such as based on the real-time signal analysis at peak one by one, but the time frame of analysis cycle or cyclic physiological signal or time period, wherein this time period crosses over two or more cycles.By repeating for the analysis of the physiological signal of each different times, by total physiological signal segmentation or be divided into several period, each period is classified separately.Thus, each selected period of this physiological signal or section are characterized by specific classification, and this classification can be used to such as indicate physiological signal quilt, such as motion artifacts, the degree polluted.Therefore, be alternative in recovery information from contaminated measurement result, have employed the signal analysis of identification and category signal section automatically, such as, to what extent represent the quality metric of physiological measurement as each signal segment of instruction.Signal analysis is with time slip-window mode operation.Cover this time window at least two cycles or slide on signal traces period, and by the value of extracting this group predefine parameter and by classifying the signal segment in each time window and analyze each time window or the signal segment in period based on the value of these extracted parameters.This method provide the method for the improvement of the trend analysis of physiological signal, it obtains reliably and the physiological signal of classification, and the one thus provided the computation-intensive without artifact physiological signal represents immediately replaces.
In an embodiment of the method in accordance with the present invention, after the method is also included in the step of this physiological signal of classification, for extracting this step of frequency through physiological signal of classifying each period.Come the frequency of execution cycle Sexual Physiology signal or the calculating of speed based on classified physiological signal, thus ensure that the reliable input for physiological signal frequency abstraction.The frequency of this physiological signal such as comprises breathing rate or heart rate, and it can be respectively defined as breathing quantity per minute or heart beating quantity per minute.
In another embodiment, after the method is also included in the step extracting this frequency, based on the value of the frequency extracted of the value of this group predefine parameter extracted from physiological signal interim time each and the physiological signal in corresponding period, for the step of extracted frequency computation part confidence value.In some cases, not only physiological signal frequency and also be also important about the information of the confidence level of calculated frequency.This information in the further process of frequency data, such as, in the frequency for reporting or the selecting properly of speed and the trend analysis of suitable frequency or speed, the input provided.
In an embodiment of the method in accordance with the present invention, this classifying step comprises be labeled as each period and accepts or refusal.Determine each period for this physiological signal by this way, whether this physiological signal interim at this time is acceptable, or in other words whether be similar to normal physiological signal and without any significant pollution, or whether this physiological signal interim at this time is unacceptable, such as, due to motion artifacts.This provide treated physiological signal, its have be classified as unacceptable or difference period, then this period can be left in the basket in the further process of physiological signal.On the other hand, treated physiological signal be classified as the reliable expression that can be considered to be the physiological signal without any remarkable interference acceptable or good period, and thus can be used as the reliable input of the further process for signal.
In a preferred embodiment, for each period being marked as refusal, the frequency of physiological signal is not extracted.Prevent the frequency using and comprise the cyclic physiological signal of the mistake in computation in period of contaminated physiological signal by this way.Be identified as well or acceptable signal segment by only processing further, thus the extraction of this frequency produces the significant value of this frequency.
In an embodiment of the method in accordance with the present invention, physiological signal represents breathing and/or the pulse of patient.Breathing and pulse represent most important physiological signal.Then the frequency of signal is represented as breathing rate and/or pulse rate.
Predefine parameter characterizes physiological signal according to time, frequency and/or locus.Can be such as signal variance, the value of peak to peak and temporal correlation according to the parameter of time.According to the parameter of frequency can be such as basic frequency and spectrum entropy.According to the parameter of locus when use multiaxis accelerometer as when physiological signal sensing equipment can be such as represent position in cartesian coordinate system axle between dependency, wherein respiratory movement occurs usually in this sensor plane, and the signal thus from the axle of two in this plane has very strong dependency, and measurement noises main with the axle of this plane orthogonal, this will help from the physiological measurement being subject to distinguishing motion radioactive content result.
In an embodiment of the method in accordance with the present invention, when having overlapping between next period and preceding epoch, not overlapping with the preceding epoch part only for next period performs this classifying step.By applying the overlapping of two periods in succession, achieving the temporal resolution of improvement, and improving reliability and the robustness of classification.Such as, when two in succession period overlapping 50%, a half data in current period from preceding epoch, this preceding epoch oneself through analyzed in previous steps and classification, and the half data in current period is new and not analyzed yet.Classifying step use the value of the parameter derived for whole current period but only by the new part classifying in this period or be labeled as such as good or difference.The good tradeoff can propagated for the temporal resolution of classification and the motion artifacts on period in succession optimizes the amount over overlap in succession between period.
According in the embodiment of the inventive method, this classifying step is also based on the staqtistical data base of this group predefine parameter.Input data set for the increase of classifying step provides the reliability of the improvement of physiological signal classification.
In an embodiment of the method in accordance with the present invention, before the method is also included in the step of repeatedly collecting physiological signal, regulate the step of this physiological signal, the step of wherein repeatedly collecting this physiological signal comprises the signal repeatedly collected through regulating.Such as, Signal Regulation comprise filter this physiological signal make the frequency corresponding with representing the frequency of this cyclic physiological signal by this filtration step.The unnecessary environmental effect which reducing noise and may reduce further this physiological signal.Such as, when the instruction of this physiological signal is breathed, this regulating step preferentially filters this physiological signal and makes the frequency corresponding with respirometric possibility frequency by this filtration step.In this case, the frequency in the frequency range between 0Hz and 2Hz is preferentially by this filtration step.As another example, when the cardiomotility of physiological signal assignor, this filtration step preferential filtered acceleration meter signal makes the frequency corresponding with the possible frequency that cardiomotility moves pass through filter element, such as when using acceleration transducer, this regulating step can be suitable for filtering frequency in frequency range that physiological signal makes between 5Hz and 20Hz by this filtration step, this is because accelerometer can capture the mechanical vibration caused by beating heart in this frequency range, this corresponds to occur or about 30 to 240 pulsatile heart rates per minute in the scope between about 0.5Hz to 4Hz.After the envelope obtaining signal after filtration, the band filter between application 0.5Hz and 4Hz is to produce the signal being used for heart rate and calculating.Also the combination of breathing and cardiomotility filters can be realized.By regulating this signal before this signal of analysis, such as, by noise filtering and/or signal normalization, filtering gross contamination thus that be improved and signal analysis that is more reliable and robust.
In an embodiment of the method in accordance with the present invention, collect multiple physiological signal simultaneously, and extract and classifying step for each the execution respectively of the plurality of physiological signal.Such as, the physiological signal measured at the diverse location place of patient body may make different reactions to motion artifacts, thus within corresponding period, produce the complementary physiological signal through classification, which results in increase and the robust and reliably classification more of useful physiological signal availability in time.
In a second aspect of the present invention, provide a kind of device for monitoring periods Sexual Physiology signal, described device comprises:
-sensor, it is suitable for measuring described cyclic physiological signal;
-period definition unit, it is suitable for the period repeatedly defining two or more cycles containing described cyclic physiological signal, physiological signal described in interim analysis when described, wherein next period contiguous preceding epoch or overlapping with preceding epoch;
-extraction unit, it is suitable for the value extracting one group of predefine parameter from the described physiological signal in described period, and described parameter characterizes the described physiological signal in described period according to time, frequency and locus; And
-taxon, it is suitable for the described physiological signal of classifying in described period based on the value of this extracted group predefine parameter.
In the embodiment of device according to the present invention, described sensor comprises multiaxis accelerometer, and measured physiological signal comprises the multiple subsignals corresponding to the plurality of axle, and synchronously analyzes these subsignals in time.Multiaxis accelerometer is the equipment of acceleration measurement in multiple sensitive axis, and such as can be used as inclinometer with reflection by breathing the abdominal part or chest exercise that cause, or with the mechanical vibration of the reflection cardiomotility measuring body surface.Multiaxis accelerometer is such as three axis accelerometer, and it is suitable for producing three accelerometer signal of instruction along the acceleration of three orthogonal space countershafts, and wherein period, definition unit was suitable for combining these three accelerometer signal to analyze the signal of combination.Preferably this multiaxis accelerometer is suitable for being positioned on the body part of people, and wherein measured signal is the breathing of assignor and the motor message of at least one of cardiomotility.In order to produce the motor message that instruction is breathed, this multiaxis accelerometer is preferably located at arcus costarum place, greatly about middle position and the centre of lying on one's side between position.But this multiaxis accelerometer also can be positioned at other positions, such as, on abdominal part, especially such as, at the restriction owing to body shape, postoperative wound, when applicable.In order to produce the motor message of instruction heart rate, this multiaxis accelerometer is preferably located at the left side of abdominal part/chest.Also preferably this accelerometer is positioned at arcus costarum place, specifically, at the cartilage place of lower-left rib.Another optimum position for generation of the multiaxis accelerometer of the motor message of instruction heart rate is the higher position on chest or the lower position on abdominal part.Particularly, be also preferred for determining to indicate the optimum position of the motor message breathed to measure the motor message of instruction heart rate.Especially, breathe and the motor message of heart rate to produce instruction, this multiaxis accelerometer is preferably located at arcus costarum place, the centre of the central authorities in left side-lie on one's side.
In the embodiment of device according to the present invention, described device also comprises frequency determinative elements, and it is for determining the value of the frequency of the physiological signal through classification.In a preferred embodiment, described device comprises multiple sensor and is suitable for measuring multiple physiological signal, and each physiological signal is analyzed respectively or in combination.
Should be understood that, the preferred embodiments of the present invention also can be the combination in any of dependent claims and corresponding independent claims.
Accompanying drawing explanation
With reference to (one or more) described below embodiment, these and other aspects of the present invention will become apparent and be elaborated.In the accompanying drawings:
Illustrate to flowchart illustration shown in Fig. 1 the embodiment of the method for the treatment of cycle Sexual Physiology signal;
The schematic example with schematically illustrating for block signal analytic definition period of Fig. 2;
Illustrate to flowchart illustration shown in Fig. 3 another embodiment of the method for the treatment of cycle Sexual Physiology signal;
Illustrate to flowchart illustration shown in Fig. 4 another embodiment of the method for the treatment of cycle Sexual Physiology signal;
Illustrate to flowchart illustration shown in Fig. 5 another embodiment of the method for the treatment of cycle Sexual Physiology signal;
Fig. 6 schematically and schematically illustrate the example of classification of cyclic physiological signal;
Fig. 7 schematically and schematically illustrate another example of classification of the cyclic physiological signal measured by three axis accelerometer; And
The schematic embodiment with schematically illustrating the device being suitable for treatment cycle Sexual Physiology signal of Fig. 8.
Detailed description of the invention
Illustrate to flowchart illustration shown in Fig. 1 the embodiment of the method for the treatment of cycle Sexual Physiology signal.In step 1, use sensor on the appropriate location being arranged in object (be people at this example) to catch cyclic physiological signal in this embodiment.But the patient in this people's hospital intensive care portion, but the patient that also can be public ward portion of hospital, the patient in this public ward portion of hospital is more mobilely and comparatively strictly do not monitored compared with under Intensive Care Therapy environment.In addition, this people can be arranged in the home environment of himself.Sensor such as can comprise the multiaxis accelerometer being suitable for producing accelerometer signal, and the instruction of this accelerometer signal is along the acceleration of different spaces axle.In this embodiment, this multiaxis accelerometer is suitable for producing the three axis accelerometer of instruction along three accelerometer signal of the acceleration of three orthogonal space countershafts.Such as, the three axis accelerometer of STMicroelectronicsLIS344ALH or KionixKXM52 by name can be used.But, the multiaxis accelerometer of other types also can be used to produce the accelerometer signal of instruction along the acceleration of different spaces axle.But the breathing of cyclic physiological signal people or heart beating.Breathing rate is one of most important vital sign in patient-monitoring, and it has been proved to be the good instruction that status of patient worsens, and it such as, in conjunction with other vital signs, heart rate, plays extremely important effect in early days in early warning hospital system.
In the step 2 of Fig. 1, definition period and at that for the moment interim collection physiological signal.This period contains two or more physiological signals cycle, preferably at least five cycles.According to the type of monitored physiological signal, typically be several seconds this period or tens seconds.Such as, classify and may be selected to be this period from 30 seconds to one minute, wherein can contain about 5 to 30 breathings for breath signal, this is the exemplary frequency range breathed.
In step 3 of figure 1, from the signal segment in the period defined in step 2, extract the value of one group of predefine parameter.This parameter and their value are from such as time, frequency and space coordinates, that is, the various aspects of the position in space, characterize the signal segment in current period.In order to grasp the specific characteristic of such as breathing or heartbeat signal, the particular characteristics of this signal can be defined.Such as signal variance, the value of peak to peak and temporal correlation according to the characterisitic parameter of time.According to the characterisitic parameter of frequency be such as basic frequency and spectrum entropy.It is such as the dependency between three the orthogonal space countershafts measured with three axis accelerometer according to the characterisitic parameter of space coordinates.By the typical respiration signals of accelerometer measures, there is low signal variance, with the frequency of 0.05Hz to 2Hz scope for the cycle, and there is very strong between centers dependency.Each signal segment is mapped on the point in parameter space by the value of the parameter extracted.
In the step 4 of Fig. 1, based on this group predefine parameter extracted in step 3 value and by the signal segment classification in period of defining in step 2.But the signal segment in this classification current period simply good-difference class, whether the signal of its instruction in current period is similar to such as breath signal.This classification also can obtain being subject to about physiological signal, such as motion artifacts, the instruction of the degree of pollution.
In the step 10 of Fig. 1, verify whether should define next period.If need not define next period, such as, owing to reaching the end of signal, so the method stops in a step 11.If and/or can should define next period, so the method returns step 2 and defines next period.This next period can be adjacent to previous period.Alternatively, this next period and preceding epoch overlapping.By applying the overlapping of two periods in succession, the temporal resolution improved can be realized.After defining next period, the method proceeds step 3, the value of one group of predefine parameter is wherein extracted for the signal segment in this period, then be step 4, wherein based on this group predefine parameter extracted in previous steps value and will be positioned at this period signal segment classification.When this period and preceding epoch overlapping, such as when two in succession period overlapping 50%, a half data in current period is from analyzed in previous steps and the preceding epoch be classified, and in current period, only has the data of half to be new and not analyzed.Classifying step uses the value of the parameter derived for whole current period in this case, but only by the new part classifying in this period or be labeled as such as good or difference.
Fig. 2 schematically and schematically illustrate the example of the breath signal 30 according to the time, the wherein period for block signal analytic definition.In Fig. 2, the trunnion axis of curve chart represents time of arbitrary unit and vertical axis represents the amplitude of the breath signal of arbitrary unit.Fig. 2 illustrates this embodiment and applies in succession overlapping period.First period and the second period overlapping, and the second period and the 3rd period overlapping.Other periods in the plot after not shown then period.According to the method illustrated in the flow chart of Fig. 1, defined for the first period first in step 2.In the step 3 and 4 of Fig. 1, analyze the part of the breath signal 30 in this first period, comprise the value first extracting first group of predefine parameter, then by this part classifying of the signal 30 in this first period.In next one circulation, defined for the second period, and collect the data of the part of the signal 30 in this second period, and use it for the value extracting second group of predefine parameter for the signal segment in this second period.Due to classified in previous classifying step first time the interim part overlapping with the second period, therefore in this embodiment, in a category signal 30 in the part in the second period not with the part that the first period is overlapping.Then defined for the 3rd period, and be collected in the data of the part of the signal 30 in the 3rd period, and use it for the value extracting the 3rd group of predefine parameter for the signal segment in this 3rd period.Due to classified in previous classifying step second time the interim part overlapping with the 3rd period, therefore in this embodiment, in a category signal 30 in the part in the 3rd period not with the part that the second period is overlapping.Repeat these steps until with containing whole signal 30 (not shown) period in succession.The good tradeoff can propagated for the temporal resolution of classification and the motion artifacts on period in succession optimizes the amount over overlap in succession between period.This period of the time period of each signal 30 of having classified slides thus provides signal analysis and the classification of segmentation on signal 30.
Illustrate to flowchart illustration shown in Fig. 3 another embodiment of the method for the treatment of cycle Sexual Physiology signal.In Fig. 3, illustrated method is the expansion of graphic technique in FIG.The step 1,2,3,4,10 and 11 of Fig. 1 is the same step in Fig. 3.In this embodiment, after step 1, step 12 provides the adjustment to the signal of catching in step 1.The adjustment of signal can comprise, such as, filter signal with the quality improving signal before analytic signal in the next step of the method.Such as, the filtration that step 12 comprises signal makes to pass through with the frequency of breathing or the possible frequency of cardiomotility is corresponding.Particularly, filtration step can be suitable for filtration frequencies the typical heart rate between 0.5Hz and 4Hz or frequency range are passed through.It should be noted that this corresponds to occur or about 30 to 240 pulsatile heart rates per minute in the scope between about 0.5Hz to 4Hz by the dirty mechanical vibration caused of pulsatile heart in the frequency range that accelerometer can capture between about 5Hz and 20Hz.After the envelope obtaining signal after filtration, application filters the band filter of the frequency range of heart rate to produce the signal being used for heart rate and calculating.Also may in order to determine that respiratory frequency filters the signal of frequency range between 0Hz and 2Hz, and trap signal makes only by the frequency range between about 0.5Hz and 4Hz in order to determine heart rate frequency.The combination of these two frequency ranges or another frequency range are also optional.In this embodiment, in step 2, collect the physiological signal be conditioned selected period.
In the step 5 of Fig. 3, be good or difference based on the signal segment determined in current period the classification results of signal segment in previous steps 4, that is, will be accepted or be rejected.Such as when breath signal, signal segment in current period is similar to breath signal, classified as well or acceptable, and the motion artifacts caused due to the body kinematics of such as people when it pollutes and is not similar to breath signal, it is poor or unaccepted to be categorized as.If accept the signal segment in current period in steps of 5, so in step 6 for frequency or the speed of the part computing cycle signal of the signal in current period.Due to signal segment be classified as well and be thus similar to required physiological signal, the frequency therefore calculated for this signal segment or the value of speed will be reliable values.Such as, when breath signal, calculate breathing rate in step 6 and calculate pulse rate when heartbeat signal.The breathing rate calculated and/or pulse rate can be shown (not shown) over the display.On the other hand, if refuse the signal segment in current period in steps of 5, so by the frequency that do not calculate for the signal segment in this period or speed.In this case, the signal in current period is not similar to required physiological signal, such as, breathe or heartbeat signal, and will cause inaccurate value for the breathing rate of this signal segment or the calculating of pulse rate.In order to strengthen the observability being rejected signal segment, can by illustrate the display according to the signal of time contains on the signal in this period coloured band and by this time interim signal segment be labeled as poor or unaccepted.
Various sorting algorithm can be used carry out/difference signal classification.Normally used grader comprises rule-based, Bayesian Method, artificial neural network, decision tree, linear discriminant function and k nearest neighbor classifier.The Choice and design of the specific grader of this breathing can comprise the use in the breath data storehouse of statistically complete mobile object, uses it to train selected grader and to evaluate its classification performance.The computational complexity of such as algorithm and the standard of interpretability also may be important for the selection of grader.Grader is as a result usually the good tradeoff of multiple standard.Notice/differ from that the generation of grader is that off-line completes, and the execution of classification is instant and operand is few, makes real-time physiological signal analysis become possibility.
Illustrate to flowchart illustration shown in Fig. 4 another embodiment of the method for the treatment of cycle Sexual Physiology signal.In Fig. 4, illustrated method is the expansion of graphic technique in figure 3.In Fig. 3, the step of same numbering is the same step in Fig. 4.In this embodiment, after step 6, in step 7, under signal segment is classified as acceptable situation, the confidence index of institute's calculated rate is calculated for the part of this signal in current period.This confidence index indicates confidence level or the precision of the frequency calculated in step 6.The value of the parameter extracted for corresponding signal section in step 4 can be used also to combine the frequency values calculated, the value of such as breathing rate or heart rate, define confidence index.Confidence index in the further process of breathing rate or heart rate data, such as, in the correct report and suitable breathing rate trend analysis of breathing rate, the input provided.
Illustrate to flowchart illustration shown in Fig. 5 another embodiment of the method for the treatment of cycle Sexual Physiology signal.In Fig. 5, illustrated method is the expansion of graphic technique in the diagram.In Fig. 4, the step of same numbering is the same step in Fig. 5.In Figure 5, accelerometer lock-on signal is used in step 1.When degree of will speed up meter is used as sensor device, not only can measures and breathe but also heart beating or pulse information can be measured.This depicts in Figure 5, wherein contiguous existed comprise step 3,4,5, the branch of 6 and 7 is incorporated with the branch being exclusively used in cardiac signal analysis, in this embodiment, in the branch that this has existed, analyzes breath signal.Step 12 is suitable for using wave filter to carry out filtered acceleration meter signal in this embodiment, and this wave filter is for filtering for determining the accelerometer signal of breathing and both the accelerometer signal for determining heart rate.Alternatively, can apply two independent special filters, the first wave filter is for filtering for determining the accelerometer signal of breathing, and the second wave filter is for filtering the accelerometer signal for determining heart rate.Because heart beating causes the mechanical vibration (and small inclination change) of the body surface measured as inertial acceleration by sensor, therefore in than the frequency band higher for breath signal, process pulse signal.
In the step 13 of Fig. 5, from the pulse signal section in the period defined in step 2, extract the value of one group of predefine parameter.Parameter and their value be the upper pulse signal section characterized in current period of such as time, frequency and space coordinates (that is, the position in space) in all its bearings.In order to grasp the specific characteristic of heartbeat signal, the particular characteristics of this signal can be defined.The characterisitic parameter according to the time of pulse signal is such as signal variance, signal average and temporal correlation.According to the characterisitic parameter of frequency be such as basic frequency and spectrum entropy.It is such as the dependency between three the orthogonal space countershafts measured with three axis accelerometer according to the characterisitic parameter of space coordinates.By accelerometer measures to typical pulse signal with the frequency of 0.5Hz to 4Hz scope for the cycle, and there is very strong between centers dependency.It should be noted that the mechanical vibration caused by beating heart in the frequency range that accelerometer can capture between about 5Hz and 20Hz, this beating heart corresponds to occur or about 30 to 240 pulsatile heart rates per minute in the scope between about 0.5Hz to 4Hz.After the envelope obtaining signal after filtration, application filters the band filter of heart rate frequency scope to produce the signal being used for heart rate and calculating.It should be noted that the definition in period in step 2 also can be made for pulse signal and breath signal respectively.
In the step 14 of Fig. 5, based on the value of this group predefine parameter extracted in step 13, with with step 3 in for do similar of breath signal but mode that is that be optimized for heart beating or pulse signal and adjust, the pulse signal section in the period that defines in step 2 of classifying.In the step 15 of Fig. 5, based on the classification results in previous steps 14, but with the similar mode adjusted for pulse signal characteristic at present done for breath signal in step 5, determine that the pulse signal section in current period is good or difference, that is, be accepted or be rejected.If accept the pulse signal section in current period in step 15, so in step 16 for the signal be in current period this part with step 6 in calculate the similar mode of breathing rate to calculate pulse rate.Due to pulse signal section be classified as well and be thus similar to typical heartbeat signal, the value of the pulse rate therefore calculated for this signal segment will be reliable value.The breathing rate calculated and pulse rate can be shown (not shown) over the display.On the other hand, if refuse the pulse signal section in current period in step 15, so the pulse rate for the pulse signal section in this period will do not calculated.In this case, the pulse signal in current period is not similar to heartbeat signal, and will cause inaccurate pulse rate values for the calculating of the pulse rate of this pulse signal section.Similarly, in steps of 5 when the signal segment in current period is not similar to breath signal, breathing rate will not be calculated.Also possible that in the same period in accept heartbeat signal and refuse breath signal, or vice versa.After step 16, in step 17, under pulse signal section is classified as acceptable situation, for this part of this pulse signal in present period, with with step 6 in calculate the similar mode of breathing rate confidence index for breath signal section, calculate the pulse rate confidence index of calculated pulse rate.This pulse rate confidence index indicates confidence level or the precision of the pulse rate calculated in step 16.The pulse rate values that the value of the parameter extracted for corresponding pulse signal section at step 14 can be used also to combine calculate is to define pulse rate confidence index.Pulse rate and breathing rate confidence index in the further process of breathing rate or pulse rate data, such as, in the correct report of breathing rate and/or pulse rate and suitable breathing rate and/or pulse rate trend analysis, the input provided.
Fig. 6 is schematic and schematically illustrate the example of application according to the classification of the breath signal 40 of the inventive method.In Fig. 6, the trunnion axis of curve chart represents time of arbitrary unit and vertical axis represents the amplitude of the arbitrary unit of breath signal 40.Shade or coloured band instruction is classified and/or is marked as poor or unaccepted signal segment 41.Breath signal 40 not in signal segment 41 be classified as well or can received signal segment.From Fig. 6 very it is clear that unaccepted signal segment 41 is subject to the pollution of such as motion artifacts, and not unaccepted signal segment reflects breath signal better than unaccepted signal segment 41.
Fig. 7 schematically and schematically illustrate another example of classification of the breath signal 52,53,54 measured by three axis accelerometer.Three curve charts are shown in Fig. 7, have eachly reflected the additional space axle for three axis accelerometer and the breath signal of catching.In Fig. 7, the trunnion axis of each curve chart represents the time of arbitrary unit, and vertical axis represents the amplitude of the x-axis accelerometer breath signal 52 of catching for space x-axle in figure 7 a, represents the amplitude of the y-axis accelerometer breath signal 53 of catching for space y-axle in fig .7b, and represents the amplitude of the z-axis accelerometer breath signal 54 of catching for space z-axle in figure 7 c.Can for the executed in parallel signal analysis of each signal 52,53,54, wherein can carry out comparison between three different classification results to provide the assembled classification of current period in classifying step.Alternatively, a signal that can be combined into three signals 52,53,54 is to perform classification.Shade or coloured band instruction is classified and/or is marked as unaccepted signal segment 51, and from it very it is clear that unaccepted signal segment 51 is subject to the pollution of such as motion artifacts, and not unaccepted signal segment reflects breath signal better than unaccepted signal segment 51.
Can run at the computer program according to the step limited in the inventive method by being suitable for performing according to method of the present invention.
The schematic embodiment with schematically illustrating the device being suitable for treatment cycle Sexual Physiology signal of Fig. 8.Sensor 101 catches cyclic physiological signal from the object of such as patient.The cyclic physiological signal caught is imported into definition unit 102 in period, for defining period in this definition unit in period in period of collecting signal data.Period, definition unit 102 was suitable for repeatedly defining period in succession, wherein next period contiguous preceding epoch or overlapping with preceding epoch.At this time, the part of interim comprised signal is imported into extraction unit 103, and extraction unit extracts the value of one group of predefine parameter from the signal segment in the current period being collected in period and having defined definition unit 102.The value of parameter that this parameter and their value define characteristic specific to this physiological signal and extract for current demand signal section, and thus characterize the physiological signal section in current period.The value of the parameter extracted is imported into taxon 104, taxon based on extracted parameter value and by the Modulation recognition in current period.In this embodiment, the signal be classified is imported into frequency computation part unit 105, and frequency computation part unit based on classification results, and optionally also based on the value of extracted parameter, calculates the frequency of the cyclic physiological signal be classified.
Monitoring device preferably includes for being positioned at the person with complimentary positions, the one or more multiaxis accelerometers preferably on the chest and/or abdominal part of people, particularly two three axis accelerometers, breathes and/or heart rate particularly to monitor under the condition of movement.This multiaxis accelerometer is used as inclinator with the motion of reflection object, particularly reflects by breathing and/or cardiomotility and the motion of the abdominal part caused or chest.This motion is reflected by the change of pitch angle of subject surface, and multiaxis accelerometer location on the object.Several spatial axes of multiaxis accelerometer, are preferably three orthogonal axles, and record accelerometer signal, this signal equals the projection in each in these axles of gravitational vectors.Preferably, extraction unit and taxon are suitable for the signal analyzing this one or more multiaxis accelerometer concurrently.
May be used for patient-monitoring according to monitoring device of the present invention and signal analysis method, detecting the severe case outside Intensive Care Therapy region especially for helping.
Although in embodiment described above, described multiaxis accelerometer preferably has three normal axis, and described multiaxis accelerometer also can have two normal axis or the axle more than three.In addition, spatial axes also can have other angle, and that is, in another embodiment, axle can be non-orthogonal.
Although in embodiment described above, employ one or two multiaxis accelerometer, the accelerometer more than two also can be used to be used for determining breathing rate and/or heart rate.
By research accompanying drawing, open and claims, those skilled in the art put into practice advocate of the present invention time, be appreciated that and realize other modification of disclosed embodiment.
In the claims, wording " comprises " does not get rid of other elements or step, and indefinite article "a" or "an" is not got rid of multiple.
Single unit or equipment can be implemented in several functions described in claim.This recording some measure in mutually different dependent claims only has the fact, does not represent and the combination of these measures can not be used.
Computer program can be stored/distributed on suitable medium, such as to provide together with other hardware or as the optical storage mediums of other hardware parts or solid state medium, but it also may with other formal distributions, such as, via the Internet or other wired or wireless telecommunication systems.
Any Reference numeral in the claims should not be construed as the restriction to scope.
Claims (14)
1. the method for a treatment cycle Sexual Physiology signal (30,40,52,53,54), said method comprising the steps of:
-containing described cyclic physiological signal (30,40,52,53,54) time interim repeatedly collection (2) the described physiological signal (30 in two or more cycles, 40,52,53,54), wherein, next period contiguous preceding epoch or overlapping with preceding epoch;
-from the described physiological signal (30 in each period, 40,52,53,54) (3 are extracted in, 13) value of one group of predefine parameter, described parameter characterizes the described physiological signal (30,40,52 in described each period according to time, frequency and locus, 53,54); And
-described the physiological signal (30,40,52,53,54) of classifying in (4,14) described each period based on the value of this extracted group predefine parameter.
2. the method for claim 1, is also included in described classification (4,14) described physiological signal (30,40,52,53,54) after step, the physiological signal (30 through classification is extracted for each period, 40,52,53,54) step of frequency (6,16).
3. method as claimed in claim 2, after being also included in the step of the described frequency of described extraction (6,16), based on from described physiological signal (30,40,52 interim time each, 53,54) the described value of described group of predefine parameter extracted in and the described physiological signal (30,40,52 in corresponding period, 53,54) value of the frequency extracted, for the step of extracted frequency computation part confidence value (7,17).
4. the step (4,14) of the method for claim 1, wherein described classification comprises each labelling in period (5,15) for accepting or refusal.
5. method as claimed in claim 4, wherein, for each period being marked as refusal, does not extract the described frequency of described physiological signal (30,40,52,53,54).
6. the method for claim 1, wherein described physiological signal (30,40,52,53,54) represents breathing and/or the pulse of patient.
7., when the method for claim 1, wherein having overlapping between next period described and described preceding epoch, only perform the step (4,14) of described classification for not overlapping with the described preceding epoch part in next period described.
8. the step (4,14) of the method for claim 1, wherein described classification is also based on the staqtistical data base of described group of predefine parameter.
9. the method for claim 1, repeatedly collects (2) described physiological signal (30,40 described in being also included in, 52,53,54) before step, regulate (12) described physiological signal (30,40,52,53,54) step, wherein, described repeatedly collection (2) described physiological signal (30,40,52,53,54) step comprises the signal repeatedly collected through regulating.
10. the method for claim 1, wherein collect multiple physiological signal (52 simultaneously, 53,54), and wherein, respectively for the step of the described extraction of each execution and the step of described classification of described multiple physiological signal (52,53,54).
11. 1 kinds of devices for monitoring periods Sexual Physiology signal (30,40,52,53,54), described device comprises:
-sensor (101), it is suitable for measuring described cyclic physiological signal (30,40,52,53,54);
In definition unit in-period (102), it is suitable for repeatedly defining contains described cyclic physiological signal (30,40,52,53,54) period in one or more cycles, physiological signal (30 described in interim analysis when described, 40,52,53,54), wherein, next period contiguous preceding epoch or overlapping with preceding epoch;
-extraction unit (103), it is suitable for from the described physiological signal (30 in each period, 40,52,53,54) value of one group of predefine parameter is extracted in, described parameter characterizes the described physiological signal (30,40,52 in described each period according to time, frequency and locus, 53,54); And
-taxon (104), it is suitable for the described physiological signal (30,40,52,53,54) of classifying in described each period based on the value of this extracted group predefine parameter.
12. devices as claimed in claim 11, wherein, described sensor (101) comprises multiaxis accelerometer, and wherein, measured physiological signal (30,40,52,53,54) the multiple subsignals (52,53 corresponding to multiple axle are comprised, 54), and described subsignal (52,53,54) is synchronously analyzed by the time.
13. devices as claimed in claim 11, also comprise frequency determinative elements (105), and it is for determining the value of the described frequency of the physiological signal (30,40,52,53,54) through classification.
14. devices as claimed in claim 11, wherein, described device comprises multiple sensor and is suitable for measuring multiple physiological signal (30,40,52,53,54), and each physiological signal is analyzed either alone or in combination.
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| US20200260996A1 (en) | 2020-08-20 |
| EP2533693A1 (en) | 2012-12-19 |
| EP2533693B1 (en) | 2018-08-22 |
| CN102843966A (en) | 2012-12-26 |
| WO2011098942A1 (en) | 2011-08-18 |
| BR112012019853A8 (en) | 2018-06-26 |
| US20120302900A1 (en) | 2012-11-29 |
| JP5855019B2 (en) | 2016-02-09 |
| BR112012019853A2 (en) | 2017-07-11 |
| JP2013519420A (en) | 2013-05-30 |
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